Industrial Big Data-Enabled Deep Learning: Integrating AI-Driven Adaptive Attention with LSTM for Advanced Solar Photovoltaic Power Forecasting

Abstract The escalating global deployment of Solar Photovoltaic (PV) systems highlights an urgent demand for sophisticated power output forecasting methods that leverage the vast datasets characteristic of industrial applications. Precise forecasting is essential for maintaining grid stability, optimizing energy distribution, and balancing the supply-demand equation, particularly given the unpredictable nature of solar energy sources. Long Short-Term Memory (LSTM) networks, renowned for their proficiency in capturing long-term dependencies within time-series data, have emerged as a leading solution to this forecasting challenge. However, the static attention mechanisms in traditional LSTM models are ill-equipped to handle the complex dynamics of solar power data. This paper introduces an innovative LSTM model that integrates an AI-driven adaptive attention mechanism, designed to harness the power of industrial big data for enhanced forecasting. The model dynamically identifies and emphasizes relevant temporal features, thereby increasing its responsiveness to real-time data fluctuations. Our approach, after extensive validation using a diverse set of benchmark solar PV datasets, demonstrates a remarkable improvement in forecasting accuracy over conventional LSTM models. The adaptive attention mechanism confers a twofold benefit: it enhances the model's adaptability by adjusting its focus in accordance with shifting data patterns, and it promotes interpretability by clarifying which temporal features are considered most influential by the model at any given moment. Ultimately, the integration of adaptive attention with LSTM establishes a new precedent in solar PV power forecasting and suggests broader applications across the renewable energy sector, showcasing the model's adaptability and wide-ranging potential.